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Spatial Autocorrelation of Disease Prevalence in South Korea Using 2012 Community Health Survey Data
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 Title & Authors
Spatial Autocorrelation of Disease Prevalence in South Korea Using 2012 Community Health Survey Data
Oh, Won Seob; Nguyen, Cong Hieu; Kim, Sang Min; Sohn, Jung Woo; Heo, Joon;
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 Abstract
As a basic research to investigate geographical variations of diseases, this study analyzes and compares spatial patterns of 24 different diseases in South Korea using prevalence rate data provided by Community Health Survey in 2012. Descriptive statistical analysis, global Moran’s I computation, and disease mapping were conducted to examine spatial associations and patterns of each disease. After the unique spatial patterns and distinctive spatial associations of each disease were observed, we concluded that 12 diseases displayed statistically significant spatial autocorrelation while the other 12 showed no spatial associations. This study suggests that diseases are caused by different risk factors and possess different etiological mechanisms. Furthermore, the study may lay foundation for future studies of geographical variations of disease prevalence in South Korea.
 Keywords
Spatial Autocorrelation;Moran’s I;Disease Prevalence;Community Health Survey;
 Language
Korean
 Cited by
 References
1.
Al-Ahmadi, K. and Al-Zahrani, A. (2013), Spatial autocorrelation of cancer incidence in Saudi Arabia, International Journal of Environmental Research and Public Health, Vol. 10, No. 12, pp. 7207-7228. crossref(new window)

2.
Hassarangsee, S., Nitin, K.T., and Marc, S. (2015), Spatial Pattern Detection of Tuberculosis: A Case Study of Si Sa Ket Province, Thailand, International Journal of Environmental Research and Public Health, Vol. 12, No. 12, pp. 16005-16018. crossref(new window)

3.
Kalkhan, M.A. (2011), Spatial Statistics: Geospatial Information Modeling and Thematic Mapping, CRC Press, Boca Raton, FL.

4.
Kim, Y. and Kang, S. (2015), Convergence analysis of determinants affecting on geographic variations in the prevalence of arthritis in Korean women using data mining, Journal of Digital Convergence, Vol. 13, No. 5, pp. 277-288. (in Korean with English abstract)

5.
Kim, Y., Cho, D., and Kang, S. (2014a), Analysis of factors associated with geographic variations in the prevalence of adult obesity using decision tree, Health and Social Science, Vol. 36, pp. 157-181. (in Korean with English abstract) crossref(new window)

6.
Kim, Y., Cho, D., and Kang, S. (2014b), An empricial analysis on geographic variations in the prevalence of diabetes, Health and Social Welfare Review, Vol. 34, No. 3, pp. 82-105. (in Korean with English abstract) crossref(new window)

7.
Kim, Y., Cho, D., Hong, S., Kim, E., and Kang, S. (2014c), Analysis on geographical variations of the prevalence of hypertension using multi-year data, The Korean Geographical Society, Vol. 49, No. 6, pp. 935-948. (in Korean with English abstract)

8.
Kreyszig, E. (1979), Advanced Engineering Mathematics, 4th ed., Wiley, New York.

9.
Lawson, A.B., Biggeri A.B., Boehning, D., Lesaffre, E., Viel, J.F., Clark, A., Schlattmann, P., and Divino, F. (2000), Disease mapping models: an empirical evaluation, Statistics in Medicine, Vol. 19, No. 17-18, pp. 2217-2241. crossref(new window)

10.
Maas, J., Verheij, R.A., de Vries, S., Spreeuwenberg, P., Schellevis, F.G., and Groenewegen, P.P. (2009), Morbidity is related to a green living environment, Journal of Epidemiology and Community Health, Vol. 63, No. 12, pp. 967-973. crossref(new window)

11.
Madsen, K.A., Cotterman, C., Thompson, H.R., Rissman, Y., Rosen, N.J., and Ritchie, L.D. (2015), Passive commuting and dietary intake in fourth and fifth grade students, American Journal of Preventive Medicine, Vol. 48, No. 3, pp. 292-299. crossref(new window)

12.
Oyana, T.J. and Lwebuga-Mukasa, J.S. (2004), Spatial relationships among asthma prevalence, health care utilization, and pollution sources in neighborhoods of Buffalo, New York. Journal of Environmental Health, Vol. 66 No. 8, pp. 25-37.

13.
Park, I., Kim, E., Kim, Y., and Hong, S. (2015), A study on regional variations for disease-specific cardiac arrest, Journal of Digital Convergence, Vol. 13, No. 1, pp. 353-366. (in Korean with English abstract)

14.
Seok, H. and Kang, S. (2013a), A study on the regional variation of hypertension medication rate, Journal of Digital Convergence, Vol. 11, No. 9, pp. 255-265. (in Korean with English abstract)

15.
Seok, H. and Kang, S. (2013b), A study on the regional variation factor of hypertension prevalence, Health and Social Welfare Review, Vol. 33, No. 3, pp. 210-236. (in Korean with English abstract) crossref(new window)

16.
Suzumori, N., Ebara, T., Kumagai, K., Goto, S., Yamada, Y., Kamijima, M., and Sugiura‐Ogasawara, M. (2014), Non‐specific psychological distress in women undergoing noninvasive prenatal testing because of advanced maternal age, Prenatal Diagnosis, Vol. 34, No. 11, pp. 1055-1060. crossref(new window)

17.
Wang, A., Clouston, S.A., Rubin, M.S., Colen, C.G., and Link, B.G. (2012), Fundamental causes of colorectal cancer mortality: the implications of informational diffusion, Milbank Quarterly, Vol. 90, No. 3, pp. 592-618. crossref(new window)

18.
Yang, B. (2012), Community health survey, Centers for Disease Control and Prevention, https://chs.cdc.go.kr/chs/index.do (last date accessed: 21 March 2016).

19.
Yun, J. and Choi. D. (2015), Geographically weighted regression on the characteristics of land use and spatial patterns of floating population in Seoul city, Journal of the Korean Society for Geospatial Information Science, Vol. 23, No. 3, pp. 77-84. (in Korean with English abstract)

20.
Yun, Y., Kim, E., Park, C., Ryu, B., Park, J., Ko, J., and Seo, H. (2015), The correlation between reflux esophagitis an high sensitivity C-reactive protein, Korean Journal of Family Practice, Vol. 5, No. 3, pp. 641-646. (in Korean with English abstract)